Knowledge Management systems facilitate the preservation and reuse of knowledge that organizations create, by providing the required functionality for capturing, storing and retrieving this knowledge. As such they have become increasingly important to organizations which recognize their knowledge as their primary source of competitiveness. Nevertheless, an issue that most KM systems have not yet adequately addressed is that of the management of imprecise and vague knowledge.

Such knowledge is prevalent within organizations due to the inherent imprecision and vagueness that characterizes human knowledge and language. Statements like Most of our software projects have failed, John is an expert in Knowledge Management or Company X is a fierce competitor of our company are quite common in business contexts and all contain some notion (most, expert, fierce respectively) whose meaning is not precisely defined. This lack of precision is not a problem when such knowledge is expected to be processed merely by humans, yet it becomes one when KM systems are expected to do that as well. For example, a system could never use the above knowledge in order to determine the number of the failed software projects or to suggest the company's least important competitors whereas a human (approximately) could.

In practice, the management and utilization of imprecise knowledge by a KM system is translated into two capabilities:

• The capability to model, codify and store this kind of knowledge in a machine processable and interpretable form.

• The capability to exploit the imprecision of the stored knowledge for enhancing the effectiveness of the latter's retrieval.

Here at IMC we are able to provide both of these capabilities through Knowledge Accelerator, a powerful knowledge management platform that has been the result of prototype research within the company. The core of the platform is a novel hybrid Knowledge Management framework that combines the merits and functionality of three knowledge engineering and management techniques: Case Based Reasoning, Ontologies and Fuzzy Logic. Through this combination the capture and utilization of the organization’s vague knowledge becomes a straightforward task which could significantly enhance the organization’s productivity and competitiveness.